Jobstar's Multi-lingual WhatsApp Chatbot - Simplifying Professional Services
Table of Contents
- What is a multi-lingual chatbot? What is the need for it?
- How to Build a Multi-Lingual Chatbot?
- How Chatbots Streamline HR- Professional Services
- Jobstar's Multi-Lingual Chatbot by Gallabox
- Further Reading:
Every business has customers who are located in different parts of the world speaking varied languages. When you reach your customers on their favourite messaging platform - WhatsApp, you ought to converse with them in their preferred native language to personalise the experience. Multi-lingual chatbots do just this for you and simplify your customer support operations. In this article, we will deep dive into everything you need to know about them and how Jobstar uses Gallabox as their official chat partner to simplify professional services employing this bot.
What is a multi-lingual chatbot? What is the need for it?
A multilingual chatbot as the name suggests is a chatbot that can converse in multiple languages with users. Due to the diverse nature of the customer segment in today's business environment, it is pivotal for a company to create a personalised customer experience by engaging with users in their preferred language. This particularly applies to a linguistically diverse country like India. The Indian E-market has exponentially grown to enable a large number of non-English speakers, this majority outnumbers English speaking users. So, it is befitting to say that employing a multi-lingual chatbot will increase your brand awareness two-fold to generate leads.
How to Build a Multi-Lingual Chatbot?
Creating a separate chatbot for each language is neither feasible nor economical for businesses. A multilingual chatbot is capable of supporting and conducting conversations in multiple languages to amplify your reach and scale your localization efforts. If you're still wondering how exactly a chatbot can help your business our WhatsApp Chatbot Guide and Building a no-code Chatbot is a great place to start.
Suggested Read: Multilingual Chatbots: Build Chatbots That Speak in 20+ Languages
Here's how you can build your multi-linguistically driven chatbot:
•User's Choice: Welcome your customers with a multi-lingual message and create button options within your chatbot for them to select their preferred language. This can be done by using a numbered list, scroll-through list or 1-3 button options in a bot flow.
•Pre-Define your Segment: As a business, you can create your multi-linguistic chatbot with your customer persona in mind. If your users are Hindi/Malayalam/Tamil speakers, create a welcome message and duplicate it in all the listed languages for better reach.
•Language Detection: Another method to cater to your customer segment is to employ an AI-based bot to detect the language used by the customer and take over accordingly. This feature is more advanced in comparison to standard chatbots.
How Chatbots Streamline HR- Professional Services
1. Candidate Profile Collection:
The candidate details such as name, telephone number, date of birth, qualifications and the number of hours that the candidate wants to work can be collected.
2. Apply via WhatsApp:
Chatbots can send across application forms relevant to the candidate's preference and store the data in relevant sheet fields for your business, thereby decreasing the time taken to filter through forms manually.
3. Reduce Human Intervention:
Enabling a Chatbot for professional services greatest advantage is to reduce time and effort spent by agents to flip through hundreds of excel sheets and forms. Bots simplify this time consuming mundane process within a matter of seconds to give your candidates a real-time response.
4. No More Redundancies:
Human intervention in HR processes results in 15-20% of agent-related errors. Simple mistakes like spelling errors, failure to insert a mobile number correctly or email id have repercussions that further end in a drop-off. This can be avoided by employing a bot to receive and verify data fields.
Jobstar's Multi-Lingual Chatbot by Gallabox
Problem:
• Manual agent intervention resulting in errors like wrong application forms being sent.
• Time-consuming process, in turn, resulting in delayed responses.
• Drop-offs due to language barriers.
• System of data to be sent and collected was through several excel sheets which were hard to keep track of.
• Agents used multiple mobile numbers to acquire candidates.
Solution:
• We created a multi-lingual rule-based chatbot to reduce drop-offs and reach a larger audience segment.
• The chatbot automated data collection parameters and provided the candidate with the right application forms reducing human intervention errors.
•Data collected by the bot were directed to one common database that could be accessed by all with the organisation.
• The multi-agent login feature of Gallabox helped Jobstar's agents to access conversations under one single WhatsApp number.
"Gallabox is our official chat partner. Their simple & elegant Automated Chat Response System made our business more easy & hassle free. We are Happy to use Gallabox, as all our business needs where fulfilled under one roof. Thank you Gallabox Team for your extended support."
- A. Kamalahasan, Managing Director for Jobstar
Impact:
• Facilitated an increase in candidate applications and sales by 60%.
• Reduction in manual agent intervention by 40%.
• 2000+ customers acquired within 2 months with the help of Gallabox's Chatbot which saved 10,000 minutes of human intervention.
•Jobstar's agents could easily access candidate conversations on the go via our mobile application.
To summarise traditional HR practices in the current digital environment aren't the best fit. To avoid redundancies, professional services like that of Jobstar employ multi-lingual chatbots to automate their operations to increase candidate experience, data collection and response rate thereby increasing their organisation's revenues. If you are from the professional services sector and would like to avail multi-lingual chatbots, book a demo today with us!
Further Reading:
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